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refactor CUDA part of photo module

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This commit is contained in:
Vladislav Vinogradov
2015-01-15 16:45:35 +03:00
parent df57d038b8
commit f48befc6f0
4 changed files with 93 additions and 84 deletions
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106
modules/photo/include/opencv2/photo/cuda.hpp
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@@ -64,64 +64,66 @@ BORDER_REPLICATE , BORDER_CONSTANT , BORDER_REFLECT and BORDER_WRAP are supporte
@sa
fastNlMeansDenoising
*/
CV_EXPORTS void nonLocalMeans(const GpuMat& src, GpuMat& dst, float h, int search_window = 21, int block_size = 7, int borderMode = BORDER_DEFAULT, Stream& s = Stream::Null());
CV_EXPORTS void nonLocalMeans(InputArray src, OutputArray dst,
float h,
int search_window = 21,
int block_size = 7,
int borderMode = BORDER_DEFAULT,
Stream& stream = Stream::Null());
/** @brief The class implements fast approximate Non Local Means Denoising algorithm.
/** @brief Perform image denoising using Non-local Means Denoising algorithm
<http://www.ipol.im/pub/algo/bcm_non_local_means_denoising> with several computational
optimizations. Noise expected to be a gaussian white noise
@param src Input 8-bit 1-channel, 2-channel or 3-channel image.
@param dst Output image with the same size and type as src .
@param h Parameter regulating filter strength. Big h value perfectly removes noise but also
removes image details, smaller h value preserves details but also preserves some noise
@param search_window Size in pixels of the window that is used to compute weighted average for
given pixel. Should be odd. Affect performance linearly: greater search_window - greater
denoising time. Recommended value 21 pixels
@param block_size Size in pixels of the template patch that is used to compute weights. Should be
odd. Recommended value 7 pixels
@param s Stream for the asynchronous invocations.
This function expected to be applied to grayscale images. For colored images look at
FastNonLocalMeansDenoising::labMethod.
@sa
fastNlMeansDenoising
*/
class CV_EXPORTS FastNonLocalMeansDenoising
{
public:
/** @brief Perform image denoising using Non-local Means Denoising algorithm
<http://www.ipol.im/pub/algo/bcm_non_local_means_denoising> with several computational
optimizations. Noise expected to be a gaussian white noise
CV_EXPORTS void fastNlMeansDenoising(InputArray src, OutputArray dst,
float h,
int search_window = 21,
int block_size = 7,
Stream& stream = Stream::Null());
@param src Input 8-bit 1-channel, 2-channel or 3-channel image.
@param dst Output image with the same size and type as src .
@param h Parameter regulating filter strength. Big h value perfectly removes noise but also
removes image details, smaller h value preserves details but also preserves some noise
@param search_window Size in pixels of the window that is used to compute weighted average for
given pixel. Should be odd. Affect performance linearly: greater search_window - greater
denoising time. Recommended value 21 pixels
@param block_size Size in pixels of the template patch that is used to compute weights. Should be
odd. Recommended value 7 pixels
@param s Stream for the asynchronous invocations.
/** @brief Modification of fastNlMeansDenoising function for colored images
This function expected to be applied to grayscale images. For colored images look at
FastNonLocalMeansDenoising::labMethod.
@param src Input 8-bit 3-channel image.
@param dst Output image with the same size and type as src .
@param h_luminance Parameter regulating filter strength. Big h value perfectly removes noise but
also removes image details, smaller h value preserves details but also preserves some noise
@param photo_render float The same as h but for color components. For most images value equals 10 will be
enought to remove colored noise and do not distort colors
@param search_window Size in pixels of the window that is used to compute weighted average for
given pixel. Should be odd. Affect performance linearly: greater search_window - greater
denoising time. Recommended value 21 pixels
@param block_size Size in pixels of the template patch that is used to compute weights. Should be
odd. Recommended value 7 pixels
@param s Stream for the asynchronous invocations.
@sa
fastNlMeansDenoising
*/
void simpleMethod(const GpuMat& src, GpuMat& dst, float h, int search_window = 21, int block_size = 7, Stream& s = Stream::Null());
The function converts image to CIELAB colorspace and then separately denoise L and AB components
with given h parameters using FastNonLocalMeansDenoising::simpleMethod function.
/** @brief Modification of FastNonLocalMeansDenoising::simpleMethod for color images
@param src Input 8-bit 3-channel image.
@param dst Output image with the same size and type as src .
@param h_luminance Parameter regulating filter strength. Big h value perfectly removes noise but
also removes image details, smaller h value preserves details but also preserves some noise
@param photo_render float The same as h but for color components. For most images value equals 10 will be
enought to remove colored noise and do not distort colors
@param search_window Size in pixels of the window that is used to compute weighted average for
given pixel. Should be odd. Affect performance linearly: greater search_window - greater
denoising time. Recommended value 21 pixels
@param block_size Size in pixels of the template patch that is used to compute weights. Should be
odd. Recommended value 7 pixels
@param s Stream for the asynchronous invocations.
The function converts image to CIELAB colorspace and then separately denoise L and AB components
with given h parameters using FastNonLocalMeansDenoising::simpleMethod function.
@sa
fastNlMeansDenoisingColored
*/
void labMethod(const GpuMat& src, GpuMat& dst, float h_luminance, float photo_render, int search_window = 21, int block_size = 7, Stream& s = Stream::Null());
private:
GpuMat buffer, extended_src_buffer;
GpuMat lab, l, ab;
};
@sa
fastNlMeansDenoisingColored
*/
CV_EXPORTS void fastNlMeansDenoisingColored(InputArray src, OutputArray dst,
float h_luminance, float photo_render,
int search_window = 21,
int block_size = 7,
Stream& stream = Stream::Null());
//! @} photo